Bibliographic Collection
Data source: Clarivate Analytics Web of Science (http://apps.webofknowledge.com)
Data format: Plaintext
Query: SO = “Journal of Informetrics”
Timespan: 2007-2017
Document Type: Articles, letters, review and
proceedings papers
Query data: May, 2018
Install and load bibliometrix R-package
# Stable version from CRAN (Comprehensive R Archive Network)
# if you need to execute the code, remove # from the beginning of the next line
# install.packages("bibliometrix")
# Most updated version from GitHub
# if you need to execute the code, remove # from the beginning of the next lines
# install.packages("devtools")
# devtools::install_github("massimoaria/bibliometrix")
library(bibliometrix)
Data Loading and Converting
myfile <- ("jfe_to_bibliometrix.txt")
# Converting the loaded files into a R bibliographic dataframe
M <- convert2df(file=myfile, dbsource="wos",format="plaintext")
Converting your wos collection into a bibliographic dataframe
Done!
Generating affiliation field tag AU_UN from C1: Done!
Section 1: Descriptive Analysis
Descriptive analysis provides some snapshots about the annual
research development, the top “k” productive authors, papers, countries
and most relevant keywords.
Main findings about the collection
#options(width=160)
results <- biblioAnalysis(M)
summary(results, k=10, pause=F, width=130)
MAIN INFORMATION ABOUT DATA
Timespan 1980 : 2022
Sources (Journals, Books, etc) 1
Documents 3211
Average years from publication 14.5
Average citations per documents 131.3
Average citations per year per doc 7.883
References 53200
DOCUMENT TYPES
article 3082
article; proceedings paper 66
biographical-item 1
correction 9
correction, addition 2
editorial material 45
letter 1
note 2
review 3
DOCUMENT CONTENTS
Keywords Plus (ID) 2758
Author's Keywords (DE) 5116
AUTHORS
Authors 3804
Author Appearances 7470
Authors of single-authored documents 518
Authors of multi-authored documents 3286
AUTHORS COLLABORATION
Single-authored documents 635
Documents per Author 0.844
Authors per Document 1.18
Co-Authors per Documents 2.33
Collaboration Index 1.28
Annual Scientific Production
Annual Percentage Growth Rate 4.043931
Most Productive Authors
Top manuscripts per citations
Corresponding Author's Countries
SCP: Single Country Publications
MCP: Multiple Country Publications
Total Citations per Country
Most Relevant Sources
Most Relevant Keywords
plot(x=results, k=10, pause=F)
Most Cited References
CR <- citations(M, field = "article", sep = ";")
cbind(CR$Cited[1:20])
[,1]
FAMA EF, 1993, J FINANC ECON, V33, P3, DOI 10.1016/0304-405X(93)90023-5 355
FAMA EF, 1973, J POLIT ECON, V81, P607, DOI 10.1086/260061 291
JENSEN MC, 1976, J FINANC ECON, V3, P305, DOI 10.1016/0304-405X(76)90026-X 272
JENSEN MC, 1986, AM ECON REV, V76, P323 239
NEWEY WK, 1987, ECONOMETRICA, V55, P703, DOI 10.2307/1913610 223
CARHART MM, 1997, J FINANC, V52, P57, DOI 10.2307/2329556 220
FAMA EF, 1992, J FINANC, V47, P427, DOI 10.2307/2329112 212
WHITE H, 1980, ECONOMETRICA, V48, P817, DOI 10.2307/1912934 187
AMIHUD Y, 2002, J FINANC MARK, V5, P31, DOI 10.1016/S1386-4181(01)00024-6 171
MYERS SC, 1984, J FINANC ECON, V13, P187, DOI 10.1016/0304-405X(84)90023-0 167
BLACK F, 1973, J POLIT ECON, V81, P637, DOI 10.1086/260062 151
LA PORTA R, 1998, J POLIT ECON, V106, P1113, DOI 10.1086/250042 142
KYLE AS, 1985, ECONOMETRICA, V53, P1315, DOI 10.2307/1913210 140
JEGADEESH N, 1993, J FINANC, V48, P65, DOI 10.1111/J.1540-6261.1993.TB04702.X 138
MYERS SC, 1977, J FINANC ECON, V5, P147, DOI 10.1016/0304-405X(77)90015-0 137
GOMPERS P, 2003, Q J ECON, V118, P107, DOI 10.1162/00335530360535162 133
FAMA EF, 1997, J FINANC ECON, V43, P153, DOI 10.1016/S0304-405X(96)00896-3 124
MERTON RC, 1973, ECONOMETRICA, V41, P867, DOI 10.2307/1913811 123
PASTOR L, 2003, J POLIT ECON, V111, P642, DOI 10.1086/374184 117
PETERSEN MA, 2009, REV FINANC STUD, V22, P435, DOI 10.1093/RFS/HHN053 112
Section 2: The Intellectual Structure of the field - Co-citation
Analysis
Citation analysis is one of the main classic techniques in
bibliometrics. It shows the structure of a specific field through the
linkages between nodes (e.g. authors, papers, journal), while the edges
can be differently interpretated depending on the network type, that are
namely co-citation, direct citation, bibliographic coupling. Please see
Aria, Cuccurullo (2017).
Below there are three examples.
First, a co-citation network that shows relations between
cited-reference works (nodes).
Second, a co-citation network that uses cited-journals as unit of
analysis.
The useful dimensions to comment the co-citation networks are: (i)
centrality and peripherality of nodes, (ii) their proximity and
distance, (iii) strength of ties, (iv) clusters, (iiv) bridging
contributions.
Third, a historiograph is built on direct citations. It draws the
intellectual linkages in a historical order. Cited works of thousands of
authors contained in a collection of published scientific articles is
sufficient for recostructing the historiographic structure of the field,
calling out the basic works in it.
Article (References) co-citation analysis
Plot options:
n = 50 (the funxtion plots the main 50 cited references)
type = “fruchterman” (the network layout is generated using the
Fruchterman-Reingold Algorithm)
size.cex = TRUE (the size of the vertices is proportional to
their degree)
size = 20 (the max size of vertices)
remove.multiple=FALSE (multiple edges are not removed)
labelsize = 1 (defines the size of vertex labels)
edgesize = 10 (The thickness of the edges is proportional to
their strength. Edgesize defines the max value of the
thickness)
edges.min = 5 (plots only edges with a strength greater than or
equal to 5)
all other arguments assume the default values
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Co-Citation Network", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=FALSE, labelsize=1,edgesize = 10, edges.min=5)
Descriptive analysis of Article co-citation network
characteristics
summary(netstat,k=10)
Main statistics about the network
Size 532
Density 0.005
Transitivity 0.297
Diameter 12
Degree Centralization 0.069
Average path length 4.565
Journal (Source) co-citation analysis
M=metaTagExtraction(M,"CR_SO",sep=";")
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "sources", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Co-Citation Network", type = "auto", size.cex=TRUE, size=15, remove.multiple=FALSE, labelsize=1,edgesize = 10, edges.min=5)
Descriptive analysis of Journal co-citation network
characteristics
netstat <- networkStat(NetMatrix)
NA
Section 3: Historiograph - Direct citation linkages
histResults <- histNetwork(M, sep = ";")
WOS DB:
Searching local citations (LCS) by reference items (SR) and DOIs...
Analyzing 134021 reference items...
Found 2418 documents with no empty Local Citations (LCS)
options(width = 130)
net <- histPlot(histResults, n=20, size = 5, labelsize = 4)
Legend
Section 4: The conceptual structure - Co-Word Analysis
Co-word networks show the conceptual structure, that uncovers links
between concepts through term co-occurences.
Conceptual structure is often used to understand the topics covered
by scholars (so-called research front) and identify what are the most
important and the most recent issues.
Dividing the whole timespan in different timeslices and comparing the
conceptual structures is useful to analyze the evolution of topics over
time.
Bibliometrix is able to analyze keywords, but also the terms in the
articles’ titles and abstracts. It does it using network analysis or
correspondance analysis (CA) or multiple correspondance analysis (MCA).
CA and MCA visualise the conceptual structure in a two-dimensional
plot.
Co-word Analysis through Keyword co-occurrences
Plot options:
normalize = “association” (the vertex similarities are normalized
using association strength)
n = 50 (the function plots the main 50 cited references)
type = “fruchterman” (the network layout is generated using the
Fruchterman-Reingold Algorithm)
size.cex = TRUE (the size of the vertices is proportional to
their degree)
size = 20 (the max size of the vertices)
remove.multiple=FALSE (multiple edges are not removed)
labelsize = 3 (defines the max size of vertex labels)
label.cex = TRUE (The vertex label sizes are proportional to
their degree)
edgesize = 10 (The thickness of the edges is proportional to
their strength. Edgesize defines the max value of the
thickness)
label.n = 30 (Labels are plotted only for the main 30
vertices)
edges.min = 25 (plots only edges with a strength greater than or
equal to 2)
all other arguments assume the default values
NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")
net=networkPlot(NetMatrix, normalize="association", n = 50, Title = "Keyword Co-occurrences", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=F, edgesize = 10, labelsize=5,label.cex=TRUE,label.n=30,edges.min=2)
Descriptive analysis of keyword co-occurrences network
characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=10)
Main statistics about the network
Size 2758
Density 0.012
Transitivity 0.158
Diameter 5
Degree Centralization 0.335
Average path length 2.466
Co-word Analysis through Correspondence Analysis
suppressWarnings(
CS <- conceptualStructure(M, method="MCA", field="ID", minDegree=15, clust=5, stemming=FALSE, labelsize=15,documents=20)
)
